Real-time prediction of electricity pricing signals is essential for scheduling load demand in price-directed grids. In a deregulated electricity market, this helps substantially increase the gains of utility companies and minimize the electricity cost to the consumers. This paper introduces a novel model for electricity locational marginal price forecasting primarily centered on relevance vector machine. Two different versions of relevance vector machine are used based on Gaussian radial basis function and polynomial kernels in the first stage. The performance of the model is boosted using Extreme Gradient Boosting to incorporate the stochastic changes in prices. In the second stage, the outputs of the three models are stacked using Elastic net regression and the final price is forecasted after bagging the computed values. The model is trained and tested on real-time data of New England electricity market. Specifically, data for two years from 2012 to 2013 have been collected with a resolution of one hour. The proposed model has proven to be highly accurate and computationally cheap at the same time. It has been compared with various models that have been previously proposed for electricity forecasting including relevance vector machine, multilayer perceptron, random forest regressor, support vector machine, recurrent neural network, and least absolute shrinkage and selection operator. The proposed model is found to outperform all the other mentioned models with a mean absolute error of 2.6 on the test set and is sufficiently cheap computationally with a training time of 88 s.